Monitoring or detecting the appearance of a fault within a mechanism has been the subject of a large amount of research; mention can be made of U.S. Pat. No. 6,301,572 relating to monitoring the state of engines or turbines.
The present invention applies in particular to diagnosing a mechanism by analyzing the vibrations produced, at least in part, by the operation of the mechanism.
To make the measurement, the helicopter is fitted with accelerometers that are placed on (secured to) the engine(s), the casing(s) of the transmission gearbox(es), the bearings of the shafts, and/or other points of the structure of the helicopter.
In flight, the signals delivered by the sensors can be converted into data and, where appropriate, synchronized (by signals delivered by a rotation sensor) and/or “averaged”, and then recorded on board the helicopter.
On return to the ground, the recorded data can be collated and analyzed. Interpreting this data is complex: it requires lengthy intervention by an expert.
The document “A rapid helicopter drive train fault detection using neuro-fuzzy method” (http://erf32.nlr.nl/abstracts/pdf/HU02.pdf) by Bang Tran et al. proposes applying a neuro-fuzzy logic method to the detection of breakdowns of mechanical systems on the basis of vibration signals.
Known tools for automatically analyzing such data in order to diagnose a mechanical fault in a mechanism are incomplete and imperfect; existing faults are sometimes not detected by such analysis tools, and fault warnings are sometimes wrongly generated thereby.